AGU Advances最新文献

The Coupled Model Intercomparison Project (CMIP) has demonstrated the importance of climate modeling for climate research and its usefulness for climate services. The latter has increased CMIP's operational burden, so much so that serving IPCC has become its animating force. Attempting to satisfy an operational mandate through a coordinated research project diminishes both the service and the research. Regaining the initiative will require CMIP to transition the quasi-operational system it has developed to an operational setting. Doing so would allow CMIP to focus on developing an international scientific agenda to encourage and exploit advances in climate modeling.

The Antarctic ice sheet is buttressed by floating ice shelves that calve icebergs along large fractures called rifts. Despite the significant influence exerted by rifting on ice shelf geometry and buttressing, the scarcity of in situ observations of rift propagation contributes considerable uncertainty to understanding rift dynamics. Here, we report the first-ever seismic recording of a multiple-kilometer rift propagation event. Remote sensing and seismic recordings reveal that a rift in the Pine Island Glacier Ice Shelf extended 10.53 km at a speed of 35.1 m/s, the fastest known ice fracture at this scale. We simulate ocean-coupled rift propagation and find that the dynamics of water flow within the rift limit the propagation rate, resulting in rupture two orders of magnitude slower than typically predicted for brittle fracture. Using seismic recordings of the elastic waves generated during rift propagation, we estimate that ocean water flows into the rift at a rate of at least 2,300 m³/s during rift propagation and causes mixing in the subshelf cavity. Our observations support the hypotheses that large ice shelf rift propagation events are brittle, hydrodynamically limited, and exhibit sensitive coupling with the surrounding ocean.

U.S. rice paddies, critical for food security, are increasingly contributing to non-CO₂ greenhouse gas (GHG) emissions like methane (CH₄) and nitrous oxide (N₂O). Yet, the full assessment of GHG balance, considering trade-offs between soil organic carbon (SOC) change and non-CO₂ GHG emissions, is lacking. Integrating an improved agroecosystem model with a meta-analysis of multiple field studies, we found that U.S. rice paddies were the rapidly growing net GHG emission sources, increased 138% from 3.7 ± 1.2 Tg CO₂eq yr⁻¹ in the 1960s to 8.9 ± 2.7 Tg CO₂eq yr⁻¹ in the 2010s. CH₄, as the primary contributor, accounted for 10.1 ± 2.3 Tg CO₂eq yr⁻¹ in the 2010s, alongside a notable rise in N₂O emissions by 0.21 ± 0.03 Tg CO₂eq yr⁻¹. SOC change could offset 14.0% (1.45 ± 0.46 Tg CO₂eq yr⁻¹) of the climate-warming effects of soil non-CO₂ GHG emissions in the 2010s. This escalation in net GHG emissions is linked to intensified land use, increased atmospheric CO₂, higher synthetic nitrogen fertilizer and manure application, and climate change. However, no/reduced tillage and non-continuous irrigation could reduce net soil GHG emissions by approximately 10% and non-CO₂ GHG emissions by about 39%, respectively. Despite the rise in net GHG emissions, the cost of achieving higher rice yields has decreased over time, with an average of 0.84 ± 0.18 kg CO₂eq ha⁻¹ emitted per kilogram of rice produced in the 2010s. The study suggests the potential for significant GHG emission reductions to achieve climate-friendly rice production in the U.S. through optimizing the ratio of synthetic N to manure fertilizer, reducing tillage, and implementing intermittent irrigation.

Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to cloud-masked, gray-scaled images, which were derived from false color images indicating elevated dust plumes in bright magenta. The images were obtained from the Spinning Enhanced Visible and Infrared Imager instrument onboard the Meteosat Second Generation satellite. We find up to 15% of summertime observations in West Africa and 10% of summertime observations in Nubia by satellite images miss dust plumes due to cloud cover. We use the new dust-plume data to demonstrate a novel approach for validating spatial patterns of the operational forecasts provided by the World Meteorological Organization Dust Regional Center in Barcelona. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but once trained, the reconstruction is computationally inexpensive. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.

The fracture of Earth materials occurs over a wide range of time and length scales. Physical conditions, particularly the stress field and Earth material properties, may condition rupture in a specific fracture regime. In nature, fast and slow fractures occur concurrently: tectonic tremor events are fast enough to emit seismic waves and frequently accompany slow earthquakes, which are too slow to emit seismic waves and are referred to as aseismic slip events. In this study, we generate simultaneous seismic and aseismic processes in a laboratory setting by driving a penny-shaped crack in a transparent sample with pressurized fluid. We leverage synchronized high-speed imaging and high-frequency acoustic emission (AE) sensing to visualize and listen to the various sequences of propagation (breaks) and arrest (sticks) of a fracture undergoing stick-break instabilities. Slow radial crack propagation is facilitated by fast tangential fractures. Fluid viscosity and pressure regulate the fracture dynamics of slow and fast events, and control the inter-event time and the energy released during individual fast events. These AE signals share behaviors with observations of episodic tremors in Cascadia, United States; these include: (a) bursty or intermittent slow propagation, and (b) nearly linear scaling of radiated energy with area. Our laboratory experiments provide a plausible model of tectonic tremor as an indicative of hydraulic fracturing facilitating shear slip during slow earthquakes.

In combination with dramatic and immediate CO₂ emissions reductions, net-negative atmospheric CO₂ removal (CDR) is necessary to maintain average global temperature increases below 2.0°C. Many proposed CDR pathways involve the placement of vast quantities of organic carbon (biomass) on the seafloor in some form, but little is known about their potential biogeochemical impacts, especially at scales relevant for global climate. Here, we evaluate the potential impacts and durability of organic carbon storage specifically within deep anoxic basins, where organic matter (OM) is remineralized through anaerobic processes that may enhance its storage efficiency. We present simple biogeochemical and mixing models to quantify the scale of potential impacts of large-scale OM addition to the abyssal seafloor in the Black Sea, Cariaco Basin, and the hypersaline Orca Basin. These calculations reveal that the Black Sea in particular may have the potential to accept biomass storage at climatically relevant scales with moderate changes to the geochemical state of abyssal water and limited communication of that impact to surface water. Still, all of these systems would require extensive further evaluation prior to consideration of megatonne-scale CO₂ sequestration. Many key unknowns remain, including the partitioning of breakdown among sulfate-reducing and methanogenic metabolisms and the fate of methane in the environment. Given the urgency of responsible CDR development and the potential for anoxic basins to reduce ecological risks to animal communities, efforts to address knowledge gaps related to microbial kinetics, benthic processes, and physical mixing in these systems are critically needed.

Recent marine heatwaves in the Gulf of Alaska have had devastating impacts on species from various trophic levels. Due to climate change, total heat exposure in the upper ocean has become longer, more intense, more frequent, and more likely to happen at the same time as other environmental extremes. The combination of multiple environmental extremes can exacerbate the response of sensitive marine organisms. Our hindcast simulation provides the first indication that more than 20% of the bottom water of the Gulf of Alaska continental shelf was exposed to quadruple heat, positive hydrogen ion concentration [H⁺], negative aragonite saturation state (Ω_arag), and negative oxygen concentration [O₂] compound extreme events during the 2018–2020 marine heat wave. Natural intrusion of deep and acidified water combined with the marine heat wave triggered the first occurrence of these events in 2019. During the 2013–2016 marine heat wave, surface waters were already exposed to widespread marine heat and positive [H⁺] compound extreme events due to the temperature effect on the [H⁺]. We introduce a new Gulf of Alaska Downwelling Index (GOADI) with short-term predictive skill, which can serve as indicator of past and near-future positive [H⁺], negative Ω_arag, and negative [O₂] compound extreme events near the shelf seafloor. Our results suggest that the marine heat waves may have not been the sole environmental stressor that led to the observed ecosystem impacts and warrant a closer look at existing in situ inorganic carbon and other environmental data in combination with biological observations and model output.

The Hunga-Tonga Hunga-Ha'apai volcano underwent a series of large-magnitude eruptions that generated broad spectra of mechanical waves in the atmosphere. We investigate the spatial and temporal evolutions of fluctuations driven by atmospheric acoustic-gravity waves (AGWs) and, in particular, the Lamb wave modes in high spatial resolution data sets measured over the Continental United States (CONUS), complemented with data over the Americas and the Pacific. Along with >800 barometer sites, tropospheric observations, and Total Electron Content data from >3,000 receivers, we report detections of volcano-induced AGWs in mesopause and ionosphere-thermosphere airglow imagery and Fabry-Perot interferometry. We also report unique AGW signatures in the ionospheric D-region, measured using Long-Range Navigation pulsed low-frequency transmitter signals. Although we observed fluctuations over a wide range of periods and speeds, we identify Lamb wave modes exhibiting 295–345 m s⁻¹ phase front velocities with correlated spatial variability of their amplitudes from the Earth's surface to the ionosphere. Results suggest that the Lamb wave modes, tracked by our ray-tracing modeling results, were accompanied by deep fluctuation fields coupled throughout the atmosphere, and were all largely consistent in arrival times with the sequence of eruptions over 8 hr. The ray results also highlight the importance of winds in reducing wave amplitudes at CONUS midlatitudes. The ability to identify and interpret Lamb wave modes and accompanying fluctuations on the basis of arrival times and speeds, despite complexity in their spectra and modulations by the inhomogeneous atmosphere, suggests opportunities for analysis and modeling to understand their signals to constrain features of hazardous events.